library(tidyverse)
library(biomaRt)
library(ggrepel)
library(clusterProfiler)
library(enrichplot)
# Parallel
library(BiocParallel)
register(MulticoreParam(6))
load('../data/microarray_NGS_objects.Rdata')
load('../data/top_tables.Rdata')
sva_counts <- read_tsv('../data/sva_counts.tsv.gz')
Rows: 14318 Columns: 66
ββ Column specification βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
Delimiter: "\t"
chr (1): Gene
dbl (65): GSM2944692, GSM2944693, GSM2944694, GSM2944695, GSM2944696, GSM2944697, GSM2944698, GSM2944699, GSM2944700,...
βΉ Use `spec()` to retrieve the full column specification for this data.
βΉ Specify the column types or set `show_col_types = FALSE` to quiet this message.
sample_meta_D <- sample_meta %>% filter(Sample %in% colnames(sva_counts)) %>%
dplyr::select(Sample:Section, Layout:Fusion) %>%
mutate(S2 = case_when(Section == 'OF' ~ 'OF', TRUE ~ 'OC')) %>%
unique()
box_maker <- function(table, genes, section = c('OF','OC'), type = 'temporal'){
if ('matrix' %in% class(table)){
table <- table %>%
as_tibble(rownames = 'Gene')
}
if (type == 'temporal'){
plot <- table %>%
pivot_longer(-Gene, names_to = 'Sample', values_to = 'Expression') %>%
mutate(Sample = gsub('_.*|.CEL.*','',Sample)) %>%
left_join(sample_meta_D) %>%
mutate(S2 = case_when(Section == 'OF' ~ 'OF',TRUE ~ 'OC')) %>%
filter(Gene %in% genes, S2 %in% section) %>%
#filter(Gene %in% row.names(top.table_OF_AD %>% head(10))) %>%
mutate(Fusion = factor(Fusion, levels = c('Before','During','After'))) %>%
mutate(OrgTech = paste(Organism, Technology, sep = '_')) %>%
ggplot(aes(x=Fusion, y=Expression, color = Organism, shape = Technology)) +
# geom_boxplot(aes(group = Fusion), color = 'Black', outlier.colour = NA) +
# geom_boxplot(aes(group = interaction(Organism,Fusion)), outlier.colour = NA) +
ggbeeswarm::geom_quasirandom(size = 3, alpha = 0.7) +
cowplot::theme_cowplot() +
facet_grid(~Gene + S2, scales = 'free_y') +
ggsci::scale_color_aaas() +
ylab('log2 (corrected counts)') +
stat_summary(fun=mean, geom = 'line', aes(group = OrgTech, color = Organism)) }
else {
plot <- table %>%
pivot_longer(-Gene, names_to = 'Sample', values_to = 'Expression') %>%
mutate(Sample = gsub('_.*|.CEL.*','',Sample)) %>%
left_join(sample_meta_D) %>%
mutate(S2 = case_when(Section == 'OF' ~ 'OF',TRUE ~ 'OC')) %>%
filter(Gene %in% genes, S2 %in% section) %>%
mutate(Fusion = factor(Fusion, levels = c('Before','During','After'))) %>%
filter(Fusion == 'During') %>%
mutate(OrgTech = paste(Organism, Technology, sep = '_')) %>%
ggplot(aes(x=S2, y=Expression, color = Organism, shape = Technology)) +
# geom_boxplot(aes(group = Fusion), color = 'Black', outlier.colour = NA) +
# geom_boxplot(aes(group = interaction(Organism,Fusion)), outlier.colour = NA) +
ggbeeswarm::geom_quasirandom(size = 3, alpha = 0.7) +
cowplot::theme_cowplot() +
ggsci::scale_color_aaas() +
ylab('log2 (corrected counts)') +
xlab('Section') +
stat_summary(fun=mean, geom = 'line', aes(group = OrgTech, color = Organism)) + facet_wrap(~Gene)
}
plot
}
volcano_maker <- function(df,
title="Volcano Plot",
pvalue='P.Value',
padj='adj.P.Val',
logFC='logFC',
gene_list = ''){
df$pvalue <- df[,pvalue]
df$log2FoldChange <- df[,logFC]
df$padj <- df[,padj]
df$Gene <- row.names(df)
df <- df[!is.na(df$pvalue),]
print(dim(df))
df <- df %>% mutate(Class = case_when(padj < 0.05 & abs(logFC) > 1~ "FDR < 0.05 & logFC > 1",
padj < 0.1 & abs(logFC) > 1 ~ 'FDR < 0.1 & logFC > 1',
TRUE ~ 'Not significant'))
df$GeneT <- df$Gene
if (gene_list == ''){
gene_list <- df %>% filter(padj < 0.05) %>% pull(Gene) %>% head(10)
}
df$Gene[!df$Gene %in% gene_list] <- ''
plot <- ggplot(data=df,aes(label=Gene, x = log2FoldChange, y = -log10(pvalue))) +
geom_point(aes(colour=Class)) +
scale_colour_manual(values=c("darkred", "red", "grey")) +
cowplot::theme_cowplot() +
geom_vline(aes(xintercept=-1),linetype="dotted") +
geom_vline(aes(xintercept=1),linetype="dotted") +
geom_vline(aes(xintercept=-2),linetype="dotted") +
geom_vline(aes(xintercept=2),linetype="dotted") +
geom_label_repel(max.overlaps = 100) +
xlab('logFC') + ylab('-log10(p value)') +
ggtitle(title) + cowplot::theme_cowplot()
plot
}
2021-12-13
Positive means higher expression in the After relative to the During time point (among OF samples only)
volcano_maker(top.table_OF_AD, title = 'OF: During vs After',
gene_list = c(top.table_OF_AD %>% filter(logFC > 0) %>% head(12) %>% row.names(),
top.table_OF_AD %>% filter(logFC < 0) %>% head(12) %>% row.names()))
[1] 14318 10
Warning in if (gene_list == "") { :
the condition has length > 1 and only the first element will be used
Genes with an FDR < 0.1 in this test.
top.table_OF_AD %>% as_tibble(rownames = 'Gene') %>% filter(adj.P.Val < 0.1) %>% DT::datatable()
Colored by organism. Each line is drawn for organism / technology (remember, mouse has both microarray and RNA-seq).
box_maker(sva_counts,
genes = top.table_OF_AD %>%
as_tibble(rownames = 'Gene') %>%
filter(adj.P.Val < 0.05, logFC > 0) %>% head(10) %>% pull(Gene),
section = c('OF'), type = 'temporal')
Joining, by = "Sample"
box_maker(sva_counts,
genes = top.table_OF_AD %>%
as_tibble(rownames = 'Gene') %>%
filter(adj.P.Val < 0.05, logFC < 0) %>% head(10) %>% pull(Gene),
section = c('OF'), type = 'temporal')
Joining, by = "Sample"
GSEA uses a ranked list of genes by logFC. So the p values are not used in this situation. The order is. So the GSEA is useful in situations where there are very few differentially expressed genes.
Activated terms (higher in the βAfterβ) relate to ion channels and cell adhesion. Suppressed terms (genes higher expressed in the During) relate to cell cycle and metabolism.
all_genes <- bitr(top.table_OF_AD %>% as_tibble(rownames = 'Gene') %>% filter(!grepl('RPS|RPL', Gene)) %>% pull(Gene), fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
'select()' returned 1:many mapping between keys and columns
Warning in bitr(top.table_OF_AD %>% as_tibble(rownames = "Gene") %>% filter(!grepl("RPS|RPL", :
0.01% of input gene IDs are fail to map...
all_genes <- all_genes %>% left_join(top.table_OF_AD %>% as_tibble(rownames = 'SYMBOL'), by = c('SYMBOL'))
logFC <- all_genes$logFC
names(logFC) <- all_genes$ENTREZID
logFC <- na.omit(logFC)
logFC = sort(logFC, decreasing = TRUE)
gse <- gseGO(geneList=logFC,
ont ="ALL",
keyType = "ENTREZID",
pvalueCutoff = 0.05,
OrgDb = org.Hs.eg.db,
pAdjustMethod = "BH",
eps = 0)
preparing geneSet collections...
GSEA analysis...
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.09% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
leading edge analysis...
done...
gse <- setReadable(gse, OrgDb = org.Hs.eg.db)
# change sort logic
gseF <- gse
gseF@result <- rbind(gseF@result %>% arrange(NES) %>% head(20),
gseF@result %>% arrange(NES) %>% tail(20) %>% arrange(-NES)
)
dotplot(gse, showCategory=15, split=".sign") + facet_grid(.~.sign) + cowplot::theme_cowplot()
So you can see the genes in the ontology term. The genes get βincludedβ as enriched if GSEA deems them to be ranked unusually high.
gse@result %>% as_tibble() %>% arrange(-abs(NES)) %>% filter(p.adjust < 0.05) %>% DT::datatable()
GO enrichment uses a cutoff between differentially expressed genes (FDR < 0.1 in this case) and everything else.
Loads of stuff relating to visual function and development.
diff_genes <- top.table_OF_AD %>% as_tibble(rownames = 'Gene') %>% filter(adj.P.Val < 0.1, !grepl('RPL|RPS', Gene))
eg_diff_genes <- bitr(diff_genes$Gene, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
'select()' returned 1:1 mapping between keys and columns
eg_diff_genes <- diff_genes %>% left_join(., eg_diff_genes, by = c('Gene' = 'SYMBOL'))
eg_universe = bitr(top.table_OF_AD %>% as_tibble(rownames = 'Gene') %>% pull(Gene), fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
'select()' returned 1:many mapping between keys and columns
Warning in bitr(top.table_OF_AD %>% as_tibble(rownames = "Gene") %>% pull(Gene), :
0.01% of input gene IDs are fail to map...
eg_diff_gene_list <- eg_diff_genes$logFC
names(eg_diff_gene_list) <- eg_diff_genes$ENTREZID
egoOF <- enrichGO(gene = eg_diff_genes$ENTREZID,
universe = eg_universe$ENTREZID,
OrgDb = org.Hs.eg.db,
ont = "all",
readable = TRUE)
p1 <- dotplot(egoOF, showCategory=20) + ggtitle("Dotplot for GO")
p1
NA
NA
So you can see the genes in the ontology term.
egoOF@result %>% as_tibble() %>% filter(p.adjust < 0.05) %>% DT::datatable()
Relationships between related GO terms with shared genes. Yellow means more expressed in the OF than the OC.
geneList <- eg_diff_genes$logFC
names(geneList) <- eg_diff_genes$Gene
cnet <- cnetplot(egoOF, foldChange = geneList, showCategory = 12) + scale_color_viridis_c(name = 'log2(FoldChange)')
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
cnet
# system("wget https://wikipathways-data.wmcloud.org/current/gmt/wikipathways-20211110-gmt-Homo_sapiens.gmt")
wp2gene <- read.gmt('wikipathways-20211110-gmt-Homo_sapiens.gmt')
wp2gene <- wp2gene %>% tidyr::separate(term, c("name","version","wpid","org"), "%")
wpid2gene <- wp2gene %>% dplyr::select(wpid, gene) #TERM2GENE
wpid2name <- wp2gene %>% dplyr::select(wpid, name) #TERM2NAME
ewp <- enricher(eg_diff_genes$ENTREZID,
TERM2GENE = wpid2gene,
TERM2NAME = wpid2name,
pvalueCutoff = 0.1)
ewp_plot <- dotplot(ewp, showCategory=10) + ggtitle("Dotplot for WikiPathways")
ewp_plot
ewp <- setReadable(ewp, OrgDb = org.Hs.eg.db, keyType = 'ENTREZID')
ewp@result %>% DT::datatable()
kk <- enrichKEGG(gene = eg_diff_genes$ENTREZID,
universe = eg_universe$ENTREZID,
organism = 'hsa')
dotplot(kk) + ggtitle("KEGG Pathway Enrichment")
kk <- setReadable(kk, OrgDb = org.Hs.eg.db, keyType = 'ENTREZID')
kk@result %>% DT::datatable()
devtools::session_info()
β Session info ποΈ π π¨βπ§βπ¦ βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
hash: cityscape, slightly frowning face, family: man, girl, boy
setting value
version R version 4.1.2 (2021-11-01)
os macOS Catalina 10.15.7
system x86_64, darwin17.0
ui RStudio
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2021-12-14
rstudio 2021.09.0+351 Ghost Orchid (desktop)
pandoc 2.14.0.3 @ /Applications/RStudio.app/Contents/MacOS/pandoc/ (via rmarkdown)
β Packages ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.1.2)
rvest 1.0.2 2021-10-16 [1] CRAN (R 4.1.2)
S4Vectors * 0.32.2 2021-11-07 [1] Bioconductor
sass 0.4.0 2021-05-12 [1] CRAN (R 4.1.0)
scales 1.1.1 2020-05-11 [1] CRAN (R 4.1.2)
scatterpie 0.1.7 2021-08-20 [1] CRAN (R 4.1.2)
sessioninfo 1.2.1 2021-11-02 [1] CRAN (R 4.1.2)
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stringi 1.7.5 2021-10-04 [1] CRAN (R 4.1.2)
stringr * 1.4.0 2019-02-10 [1] CRAN (R 4.1.2)
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survival 3.2-13 2021-08-24 [1] CRAN (R 4.1.2)
sva 3.42.0 2021-10-26 [1] Bioconductor
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tibble * 3.1.6 2021-11-07 [1] CRAN (R 4.1.2)
tidygraph 1.2.0 2020-05-12 [1] CRAN (R 4.1.2)
tidyr * 1.1.4 2021-09-27 [1] CRAN (R 4.1.2)
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tidytree 0.3.6 2021-11-12 [1] CRAN (R 4.1.2)
tidyverse * 1.3.1 2021-04-15 [1] CRAN (R 4.1.2)
treeio 1.18.1 2021-11-14 [1] Bioconductor
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viridis 0.6.2 2021-10-13 [1] CRAN (R 4.1.0)
viridisLite 0.4.0 2021-04-13 [1] CRAN (R 4.1.2)
vroom 1.5.5 2021-09-14 [1] CRAN (R 4.1.2)
withr 2.4.2 2021-04-18 [1] CRAN (R 4.1.2)
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XML 3.99-0.8 2021-09-17 [1] CRAN (R 4.1.2)
xml2 1.3.2 2020-04-23 [1] CRAN (R 4.1.0)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.1.2)
XVector 0.34.0 2021-10-26 [1] Bioconductor
yaml 2.2.1 2020-02-01 [1] CRAN (R 4.1.2)
yulab.utils 0.0.4 2021-10-09 [1] CRAN (R 4.1.0)
zlibbioc 1.40.0 2021-10-26 [1] Bioconductor
[1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
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